This paper addresses the problem of anticipating the next-active-object location in the future, for a given egocentric video clip where the contact might happen, before any action takes place. The problem is considerably hard, as we aim at estimating the position of such objects in a scenario where the observed clip and the action segment are separated by the so-called ``time to contact'' (TTC) segment. Many methods have been proposed to anticipate the action of a person based on previous hand movements and interactions with the surroundings. However, there have been no attempts to investigate the next possible interactable object, and its future location with respect to the first-person's motion and the field-of-view drift during the TTC window. We define this as the task of Anticipating the Next ACTive Object (ANACTO). To this end, we propose a transformer-based self-attention framework to identify and locate the next-active-object in an egocentric clip. We benchmark our method on three datasets: EpicKitchens-100, EGTEA+ and Ego4D. We also provide annotations for the first two datasets. Our approach performs best compared to relevant baseline methods. We also conduct ablation studies to understand the effectiveness of the proposed and baseline methods on varying conditions. Code and ANACTO task annotations will be made available upon paper acceptance.

翻译：本文探讨在自我中心视频中，针对给定视频片段，在接触可能发生但行动尚未执行之前，预测未来下一个主动目标位置的问题。该问题具有显著难度，因为我们的目标是在观测片段与行动片段之间存在称为“接触时间”间隔的场景中估计此类物体的位置。已有许多方法基于手部运动及其与环境的交互来预测人类行动。然而，目前尚无研究尝试探究下一个可能的可交互物体，及其在接触时间窗口内相对于第一人称运动与视野漂移的未来位置。我们将此定义为“下一个主动目标预测”任务。为此，我们提出基于Transformer的自注意力框架，用于在自我中心片段中识别并定位下一个主动目标。我们在EpicKitchens-100、EGTEA+和Ego4D三个数据集上对方法进行基准测试，并为前两个数据集提供标注。与相关基线方法相比，我们的方法表现最优。我们还通过消融实验研究在不同条件下所提方法与基线方法的有效性。代码及ANACTO任务标注将在论文接收后公开。